Exploration of Loan data from Prosper

library('scales')
library('memisc')

library('lattice')
library('MASS')
library('car')

library('plyr')

library('reshape')

library('GGally')
library(gridExtra)
library('RColorBrewer')
library('ggplot2')
library(dplyr)

library(rworldmap)

library(ggmap)
library(maps)

loans <- read.csv("prosperLoanData.csv")
#names(loans)
loan_data <- loans %>% 
  subset(IncomeVerifiable == "True") %>% 
  select(BorrowerAPR,BorrowerRate,LoanOriginalAmount,ProsperScore,LenderYield,EstimatedLoss,EstimatedEffectiveYield,Occupation,BorrowerState,EmploymentStatusDuration,EmploymentStatus,IsBorrowerHomeowner,CreditScoreRangeLower,CreditScoreRangeUpper,IncomeRange,StatedMonthlyIncome,Investors)
loan_data <- subset(loan_data, !is.na(BorrowerAPR))
loan_data$average_cs <- (loan_data$CreditScoreRangeUpper + loan_data$CreditScoreRangeLower) / 2
str(loan_data)

## 'data.frame':    105243 obs. of  18 variables:
##  $ BorrowerAPR             : num  0.165 0.12 0.283 0.125 0.246 ...
##  $ BorrowerRate            : num  0.158 0.092 0.275 0.0974 0.2085 ...
##  $ LoanOriginalAmount      : int  9425 10000 3001 10000 15000 15000 3000 10000 10000 10000 ...
##  $ ProsperScore            : num  NA 7 NA 9 4 10 2 4 9 11 ...
##  $ LenderYield             : num  0.138 0.082 0.24 0.0874 0.1985 ...
##  $ EstimatedLoss           : num  NA 0.0249 NA 0.0249 0.0925 ...
##  $ EstimatedEffectiveYield : num  NA 0.0796 NA 0.0849 0.1832 ...
##  $ Occupation              : Factor w/ 68 levels "","Accountant/CPA",..: 37 43 37 52 21 43 50 29 24 24 ...
##  $ BorrowerState           : Factor w/ 52 levels "","AK","AL","AR",..: 7 7 12 12 25 34 18 6 16 16 ...
##  $ EmploymentStatusDuration: int  2 44 NA 113 44 82 172 103 269 269 ...
##  $ EmploymentStatus        : Factor w/ 9 levels "","Employed",..: 9 2 4 2 2 2 2 2 2 2 ...
##  $ IsBorrowerHomeowner     : Factor w/ 2 levels "False","True": 2 1 1 2 2 2 1 1 2 2 ...
##  $ CreditScoreRangeLower   : int  640 680 480 800 680 740 680 700 820 820 ...
##  $ CreditScoreRangeUpper   : int  659 699 499 819 699 759 699 719 839 839 ...
##  $ IncomeRange             : Factor w/ 8 levels "$0","$1-24,999",..: 4 5 7 4 3 3 4 4 4 4 ...
##  $ StatedMonthlyIncome     : num  3083 6125 2083 2875 9583 ...
##  $ Investors               : int  258 1 41 158 20 1 1 1 1 1 ...
##  $ average_cs              : num  650 690 490 810 690 ...

all_states <- map_data("state")
state_list <- read.csv("states.csv")
colnames(state_list)[2] <-"BorrowerState"
loan_data2 <- left_join(loan_data,state_list, by = "BorrowerState")

loan_data2$State <- tolower(loan_data2$State)
colnames(loan_data2)[19] <- "region"

sample_loan <- select(loan_data2,BorrowerAPR,LoanOriginalAmount,region)
sample_loan$region_freq <- 1
sample_loan <- subset(sample_loan, !is.na(region))

map_loan <- sample_loan %>% 
  select(region,BorrowerAPR,LoanOriginalAmount,region_freq) %>% 
  group_by(region) %>% 
  summarise(averageAPR = mean(BorrowerAPR),averageLA = mean(LoanOriginalAmount),count = sum(region_freq))
map_loan_final <- left_join(all_states,map_loan, by ="region")

Borrower APR

ggplot(data = loan_data,aes(x = BorrowerAPR)) + geom_histogram(binwidth = 0.005, color = "tomato1",fill = "royalblue" ) 

The data seems to be well distributed. This is with a bin width of 0.005 so the bins are very fine. Due to the distribution being even, we can move along without any major transformations. The data is very well centered around the mean which can by looking at the fugure. Some of the data have high frequency which can also be seen in the figure.

BorrowerRate

ggplot(data = loan_data,aes(x = BorrowerRate)) + geom_histogram(binwidth = 0.005, color = "maroon1",fill = "seagreen2")

The borrower rate also seems to be well distributed, another interesting thing we can see in the data is that it shares a similar if not same mode as the Borrower APR. The two graphs (Borrower Rate and APR) have the same bin = 0.005, so the bins are the same size.

set.seed(5555)
loan_sample <-  select(loan_data, -IncomeRange,-CreditScoreRangeLower,-CreditScoreRangeUpper,-IsBorrowerHomeowner,-BorrowerState,-Occupation)
loan_sample <- loan_sample[sample(1:length(loan_sample$BorrowerAPR), 1000),]
ggpairs(loan_sample)

The grid has relationships of all the numerical variable that I have selected for the analyses. The first thing you might see if you are looking for trends is that there are a some data with high levels of correlation. The rates and yield seem to be very correlated. This will be my starting investigation.

LenderYield

ggplot(data = loan_data,aes(x = LenderYield)) + geom_histogram(binwidth = 0.005,fill = "greenyellow", color = "indianred")  

The Lender Yield by itself looks a lot like APR and interest rates. Yield is often associated with a return on a debt. Here the yield would be associated to the persons loaning out the money. As a whole yield seems very related to interest rate.

Mode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}
p1 <- ggplot(data = loan_data,aes(x = (BorrowerAPR))) +geom_histogram(binwidth = 0.005,fill = "white",color = "black") +
  geom_vline(xintercept = Mode(loan_data$BorrowerAPR),color = "red") + 
  geom_vline(xintercept = median(loan_data$BorrowerAPR),color = "green") +
  geom_vline(xintercept = mean(loan_data$BorrowerAPR),color = "blue") +
    scale_x_continuous(lim = c(0,0.4),breaks = seq(0,0.4,0.1))
p2 <- ggplot(data = loan_data,aes(x = (BorrowerRate))) +geom_histogram(binwidth = 0.005,fill = "white",color = "black") +
  geom_vline(xintercept = Mode(loan_data$BorrowerRate),color = "red") + 
  geom_vline(xintercept = mean(loan_data$BorrowerRate),color = "blue") +
  geom_vline(xintercept = median(loan_data$BorrowerRate),color = "green") +
  scale_x_continuous(lim = c(0,0.4),breaks = seq(0,0.4,0.1))
p3 <- ggplot(data = loan_data,aes(x = LenderYield)) +geom_histogram(binwidth = 0.005,fill = "white",color = "black") + 
  geom_vline(aes(xintercept = Mode(loan_data$LenderYield)),color = "red") + 
  geom_vline(aes(xintercept = mean(loan_data$LenderYield)),color = "blue") +
  geom_vline(aes(xintercept = median(loan_data$LenderYield)),color = "green") +
  scale_x_continuous(lim = c(0,0.4),breaks = seq(0,0.4,0.1))
grid.arrange(p1,p2,p3, ncol = 1)

They are all in the same scale and comparing the lenders yields to the interest rate and APR we see that they have similar distribution. The red line is for the mode, the blue is for the mean and the green is for the median.With the lines it can be seen that the data have similar middle values which differ by only marginal amounts.

Original Loan Amount

ggplot(data = loan_data,aes(x = LoanOriginalAmount)) + geom_histogram(binwidth = 1000,color = "red3",fill = "black")

The plots shows loan amounts that were disbursed to individual loan seekers.The business model for prosper can be seen with the help of this plot. Though still an assumption but with the graph it looks like prosper is for clients looking for small loan amounts, hence it could be prospers main market.

summary(loan_data$LoanOriginalAmount)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1000    4000    6500    8439   12000   35000

Looking at the descriptive statistics of the loan disbursement it can be seen that most of the loans are in between 4000$ to 12,000.

ggplot(data = loan_data,aes(x = log(LoanOriginalAmount))) + geom_histogram(color = "slateblue", fill = "grey") 

The distribution looks more like a bimodal distribution, there are three peaks but the two majors are high and give shape of a bimodel distribution. The data has been altered using log scale. The first data set did have a slight bit of skewness to it. What the log model does is it allows for linear regressions to happene, if a regression analysis were to be conducted. The log model is also like a scale of the magnitude of investments interms of amount not frequency.

Estimated Effective Yield and Estimated Loss

p4 <- ggplot(data = loan_sample,aes(x = EstimatedEffectiveYield)) +geom_histogram(color = "darkorchid", fill = "darkorange") 
p5 <- ggplot(data = loan_sample,aes(x = EstimatedLoss)) +geom_histogram(color = "darkorange", fill = "darkorchid")  
grid.arrange(p4,p5,ncol = 1)  

The estimated loss and yield seems to be distributed nicely as well. The only concerning factor here would the presence of some out liars which spreads the max or min for the data. The outliers for Yield seems to be negative while for estimated loss has some estimated amounts are very large in proportion which could be attributed to some of the big loan that were given through prosper. Effective yield is different from yield in terms it compounds the rates and also in this case is estimated. The negative values of yield could be a prediction for some investments not paying money.Both the variables are predictions so if consumers or investors are aware of them it could influence other variables for loan.

Occupation of a person/ State

 ggplot(data = loan_data,aes(x = Occupation)) + geom_bar(color = "navyblue", fill = "olivedrab") +coord_flip()

This is a discrete variable. The one dissapointing thing about the occupation data is that majority of the people have described themselves as others. This could seriously hamper the credibility of the data. Though the case by filtering and comparing them to other variables we may still be able to salvage some insight.

Employement Status and duration

p9 <- ggplot(data = subset(loan_data,EmploymentStatus != ""),aes(x = EmploymentStatus)) + geom_bar(color = "paleturquoise", fill = "palevioletred") 
p10 <- ggplot(data = loan_data,aes(x = EmploymentStatusDuration)) + geom_bar(fill = "paleturquoise", color = "palevioletred") 
grid.arrange(p9,p10,ncol = 1)

For employment status i had to filter out the blank data which accounts for 2000 or so slots. Looking at the duration, it gets tricky as there is a serious skewness to the data. The employed status shows that the people who are recieving loans are most employed people. This is good as it shows that prosper though not like any other loaning establishment still follows some of the norms of the market. Where this gets “abnormal” is that in terms of the employment duration we see alot of people have not worked for as long as a year but have still recieved loans. This could be due to the short time frame prosper has been active, users being young adults and recently working or because the market might have alot of retired or not working people.

ggplot(data = loan_data,aes(x = log2(EmploymentStatusDuration))) + geom_bar(binwidth = 0.1, color = "black", fill = "yellow")

This looks like a better distribution. Though statistically more friendly to look at is it of any value? What meaning does log base 2 for employment duration have for the plot or analyses. Log base 2 of x means what power of 2 would result int x. This would also be useful in a logistic analyses.Besides logistic regration it can be seen through the plot that the log 2 values of most of the days worked are centered around six. So it can be said that log 2 (Most of the employed duration) = 6.

Income range and stated monthly income

p11 <- ggplot(data = loan_data,aes(x = IncomeRange)) + geom_bar(fill = "steelblue",color = "black") 
p12 <- ggplot(data = subset(loan_data,StatedMonthlyIncome < 100000),aes(x = StatedMonthlyIncome)) + geom_bar(binwidth = 1000, fill = "black", color = "steelblue") 

grid.arrange(p11,p12,ncol = 1)

summary(loan_data$StatedMonthlyIncome)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0    3333    4750    5655    6846  483333

The stats above are for stated monthly incomes. The income range separation is well distributed. The problem at hand is the the distribution of data for stated monthly income. In the graph you see a subset of data of stated income less than 100,000. Majority of the data is centered below 10,000.The data is still strected boyond proportions.So there wierd outliers? Should they be removed?It also seems odd to see such a large amount stated as largest monthly salary. It is hard to tell for sure if those are mistakes or if they were really the case. Statistically it would seem better to work with data closer to the mean.

p13 <- ggplot(data = loan_data,aes(x = log(StatedMonthlyIncome))) + geom_bar(binwidth = 0.1,fill = "deeppink1") 

## Warning: `geom_bar()` no longer has a `binwidth` parameter. Please use
## `geom_histogram()` instead.

p14 <- ggplot(data = loan_data,aes(x = log2(StatedMonthlyIncome))) + geom_bar(binwidth = 0.1,fill = "deeppink")

## Warning: `geom_bar()` no longer has a `binwidth` parameter. Please use
## `geom_histogram()` instead.

p15 <- ggplot(data = loan_data,aes(x = log10(StatedMonthlyIncome))) + geom_bar(binwidth = 0.01, fill = "deeppink3")

grid.arrange(p13,p14,p15,ncol = 2)

Looking at three different transformations they all seem succesful in distrbuting the data.A logistic transformation has eliminated the extreme values.

ProsperScore and Credit score

p16 <- ggplot(data = loan_data,aes(x = ProsperScore)) + geom_bar(color = "firebrick1", fill = "goldenrod3") 
p17 <- ggplot(data = loan_data,aes(x = average_cs)) + geom_bar(fill = "firebrick1", color = "goldenrod3") 
grid.arrange(p16,p17,ncol =1)

The prosper score and credit score seem well distributed as well. The average credit score is an an average of a lower bund and an upper bound of credit score. Both variables seem to be factors and discrete but could be manipulated into an continuous identity.

Geographical Distribution(States)

ggplot() + geom_polygon(data= map_loan_final, aes(x=long, y=lat, group = group, fill=map_loan_final$count),colour="white") +
  scale_fill_continuous(low = "peachpuff1", high = "peachpuff4", guide="colorbar") +
  theme_bw()  +
  labs(title = "Count of loan disbursement based on prosper data across the US",fill = "count") +
  scale_y_continuous(breaks=c()) + 
  scale_x_continuous(breaks=c()) + 
  theme(panel.border =  element_blank())

Now when we look at how data has been spread across the states. California seems to have the most loan disbursements. These are all prosper data and could be explained beacuse prosper is an organization based of off california. Besides california there are quiet the amount of users in new york, texas and florida as well. The lowest amount of users were prsent in North Dokota. The other minimal users were in Maine, Wyomming and North Dokata to name a few states. The influence of prosper can be seen in about a little more than half the amount of states. The disbursemnt are given below.

summary(map_loan$count)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      50     420    1005    1959    2684   13504

Homewonership

Homeownership is a factor that can give insights into loan disbursemnt. Prosper has stated it doesnt take collateral so it could be into another insight as too whether prosper would really consider this variable or not. Ownership is a bianary output of a yes or no. There might be a good amount of manipulation and thsi could split the interpretation into two polar directions. Though it is a good insight tool and will help give key insight.

summary(loan_data$IsBorrowerHomeowner)

## False  True 
## 51170 54073

Seems to be more interesting as the data split 50/50 one could argue of even sprad and distribution as well. This could really add dimension to the model.

Investors

ggplot(data =loan_data,aes(x = Investors)) + geom_bar(binwidth = 0.1) +scale_x_continuous(lim = c(0,10),breaks = seq(0,10,1))

Most of the data has only a single investor. This skewness is unproportional. A correlation test in bivariate analyses shows no real relation to any of the variables. This could be due to the oddness of the data frequency. Very little options in remodelling leves me to drop this variable.

cor_data <- select(loan_data,-Occupation,-BorrowerState,-EmploymentStatus,-IsBorrowerHomeowner,-CreditScoreRangeLower,-CreditScoreRangeUpper,-IncomeRange)
cor_data <- subset(cor_data,!is.na(ProsperScore))
cor_data <- subset(cor_data,!is.na(EmploymentStatusDuration))
cor_result <- cor(cor_data, method = "pearson")
cor_result

##                          BorrowerAPR BorrowerRate LoanOriginalAmount
## BorrowerAPR                1.0000000   0.99344268        -0.41822571
## BorrowerRate               0.9934427   1.00000000        -0.40547431
## LoanOriginalAmount        -0.4182257  -0.40547431         1.00000000
## ProsperScore              -0.6746714  -0.65673358         0.26445293
## LenderYield                0.9934436   0.99999575        -0.40543073
## EstimatedLoss              0.9528574   0.94883823        -0.42282653
## EstimatedEffectiveYield    0.9021611   0.90162603        -0.32565609
## EmploymentStatusDuration  -0.0236893  -0.02352023         0.06730129
## StatedMonthlyIncome       -0.1568695  -0.15495373         0.30022734
## Investors                 -0.2705047  -0.24735310         0.31963067
## average_cs                -0.5468156  -0.52854073         0.28576478
##                          ProsperScore LenderYield EstimatedLoss
## BorrowerAPR              -0.674671374  0.99344360    0.95285741
## BorrowerRate             -0.656733576  0.99999575    0.94883823
## LoanOriginalAmount        0.264452932 -0.40543073   -0.42282653
## ProsperScore              1.000000000 -0.65677965   -0.68022357
## LenderYield              -0.656779653  1.00000000    0.94884888
## EstimatedLoss            -0.680223567  0.94884888    1.00000000
## EstimatedEffectiveYield  -0.642816341  0.90168465    0.81496056
## EmploymentStatusDuration -0.008279797 -0.02351152   -0.02534737
## StatedMonthlyIncome       0.156722217 -0.15495087   -0.15257092
## Investors                 0.322933531 -0.24739903   -0.28129276
## average_cs                0.387073964 -0.52855470   -0.53230202
##                          EstimatedEffectiveYield EmploymentStatusDuration
## BorrowerAPR                           0.90216113             -0.023689302
## BorrowerRate                          0.90162603             -0.023520232
## LoanOriginalAmount                   -0.32565609              0.067301290
## ProsperScore                         -0.64281634             -0.008279797
## LenderYield                           0.90168465             -0.023511521
## EstimatedLoss                         0.81496056             -0.025347368
## EstimatedEffectiveYield               1.00000000             -0.010999691
## EmploymentStatusDuration             -0.01099969              1.000000000
## StatedMonthlyIncome                  -0.13428710              0.064872948
## Investors                            -0.27288032             -0.019541931
## average_cs                           -0.46941774              0.025787264
##                          StatedMonthlyIncome   Investors  average_cs
## BorrowerAPR                      -0.15686951 -0.27050473 -0.54681559
## BorrowerRate                     -0.15495373 -0.24735310 -0.52854073
## LoanOriginalAmount                0.30022734  0.31963067  0.28576478
## ProsperScore                      0.15672222  0.32293353  0.38707396
## LenderYield                      -0.15495087 -0.24739903 -0.52855470
## EstimatedLoss                    -0.15257092 -0.28129276 -0.53230202
## EstimatedEffectiveYield          -0.13428710 -0.27288032 -0.46941774
## EmploymentStatusDuration          0.06487295 -0.01954193  0.02578726
## StatedMonthlyIncome               1.00000000  0.12276229  0.10550350
## Investors                         0.12276229  1.00000000  0.36063686
## average_cs                        0.10550350  0.36063686  1.00000000

APR vs RATE

ggplot(data = loan_data,aes(x = BorrowerAPR,y = BorrowerRate))+geom_point(alpha = 0.2, color = "pink") + geom_smooth(method = 'lm') 

Borrower APR and rate are most probably very interelated. There could be homoscedacity present amongst the two variables. This would mean that the two variables would share similar variances.

APR vs Yield

ggplot(data = loan_data,aes(x = BorrowerAPR,y = LenderYield))+geom_point(alpha = 0.2, color = "pink") + geom_smooth(method = 'lm') 

There seems to be similar relationship between these two variables as well.

Yield vs Rate

ggplot(data = loan_data,aes(x = BorrowerRate,y = LenderYield))+geom_point(alpha = 0.2, color = "orange") + geom_smooth(method = 'lm') 

This is bewteen LenderYield and borrower rate.The data seems more stuck together than any other relation with the data. Data such as these could bring multicollinearity issues.

ProsperScore vs APR

ggplot(data = loan_data,aes(y = BorrowerAPR,x = factor(ProsperScore)))+geom_boxplot() 

A look at prosper score as a discrete factor against APR show how each score recieves different interest rates. There are some rates for score which are out of the mean range and are out of the ordinary such as a Prosper score of 1 getting a rate below 20% or a prosper score of 10 getting a rate of 38%. Besides some odities it seems that a good prosper score can get you a good interest rate.

Loan Original Amount vs ProsperScore

ggplot(data = loan_data,aes(y = LoanOriginalAmount,x = factor(ProsperScore)))+geom_boxplot() 

As you can see when you compare the score with loan ammounts you see a simmilar trend where better scores give you more amounts of money. The only interseting point is that a prosper score of 8 would has higher amounts of loan disbrsemnets than of score 9.

APR vs credit score

ggplot(data = subset(loan_data, !is.na(average_cs)),aes(y = BorrowerAPR,x = average_cs))+geom_smooth()

BorrowerAPR or the interest rate seems inversely related to credit score. As the credit score gets good the rates go down.

Loan amount vs credit score

ggplot(data = subset(loan_data, !is.na(average_cs)),aes(y = LoanOriginalAmount,x = average_cs))+geom_smooth()

The relation seen is that as the cs score and loan disbursement is positive. Hence we can see that the credit score is similar to prosper score.

Credit score vs Prosper score

ggplot(data = loan_data,aes(x =ProsperScore,y = average_cs))+geom_smooth()

The relationship between prosper score and average credit score also seems to be positive and a higher credit score results in higher prosper score. Note that most credits scores are above 550 and credit score which account for prosper scores are only as low as 660.

APR vs Estimated Loss

ggplot(data = loan_data,aes(y = BorrowerAPR,x = EstimatedLoss))+geom_jitter(alpha = 0.5, color = "purple") + geom_smooth()

It can be seen that a increase in loss estimate results in an increase in rates this would probably be so the lenders can recover money. Over a certain loss amount the rates stop which means there is probably a threshold on which lenders are willing to risk money.

APR vs Estimated Effective yield

ggplot(data = loan_data,aes(y = BorrowerAPR,x = EstimatedEffectiveYield))+geom_jitter(alpha = 0.2, color = "purple") + geom_smooth(method = "lm") + coord_flip()

Yield seems to be possitive to the rates charged. This would mean that yield is an outcome of the interest rate. When we look at it as a reverse function where { f(x) = APR || [yield = f(x)] || or f(y=APR) = y(yield) / f(n) where} (f(x) = mx + c or f(n)x a linear/non linear variation) f(n) = is a numerical function from w/o variables from f(x)

Stated income vs APR

ggplot(data = subset(loan_data, StatedMonthlyIncome < 50000),aes(x = BorrowerAPR,y = StatedMonthlyIncome))+geom_jitter(alpha = 0.5, color = "purple") + geom_smooth()

With a stated monthley income it looks like it does not have much effect on the rates. The incomes have been subseted removing extreme values.

Stated Income vs Loan Amount

ggplot(data = subset(loan_data, StatedMonthlyIncome < 50000),aes(x = LoanOriginalAmount,y = StatedMonthlyIncome))+geom_jitter(alpha = 0.2, color = "grey") + geom_smooth(color = "red",linetype = 2)

The stated Monethly income has a better realtion with the original loan amount.It shows how having a larger income source enables you for a larger loan amount.

APR vs Loan amount

ggplot(data = loan_data,aes(x = BorrowerAPR,y = LoanOriginalAmount))+geom_jitter(alpha = 0.1, color = "Maroon")

The relationship between loan amount and apr doesn’t seem to be the strongest. The correlation test stated that the correlation was -0.41 which i believe is a fair prediction. The only thing to be noted is that the prediction was made for a subset of about half the data. Nonetheless it seems to be somewhere around what the original values would be. A negative relationship can be seen in the graph as well. Without subsets the relation ships is -0.3 which i believe could be because of extremes in data. The result of -0.42 can be seen when we filter out all data without prosper scores which leaves a subset of about 70,000 data.

EmployementStatus Duration vs APR

ggplot(data = loan_data,aes(x = BorrowerAPR,y = EmploymentStatusDuration))+geom_jitter(alpha = 0.1, color = "Maroon")

There seems to be a similar situation between the stated employement days and interest rate. Hence we can say that the employement duration is a non factor for the case with prosper variables and at the end of the dropping this would be my next step as it doesnt share any concrete realtion with any variable.

Average loan amounts geographically and average rates geographically

ggplot() + geom_polygon(data= map_loan_final, aes(x=long, y=lat, group = group, fill=map_loan_final$averageAPR),colour="white") +
scale_fill_continuous(low = "thistle2", high = "darkred", guide="colorbar") +
theme_bw()  +
labs(title = "Average loan rates based on prosper data across the US",fill = "Average APR") +
scale_y_continuous(breaks=c()) + 
scale_x_continuous(breaks=c()) + 
theme(panel.border =  element_blank())

For the interest rates floating around the US it can be seen that alot of the States have a high APR on their loans.

summary(map_loan$averageAPR)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1738  0.2137  0.2172  0.2174  0.2221  0.2362

The lowest average rates could be seen in the states of Maine and Iowa. The other states with low average rates include Alaska and District of columbia. Interest rates could be influenced by geographical and demographical factors as well. This would be another research. Surprisingly high interest rates were in places like alabama and Arkansas. I was expecting states like new york and new New Jersey having high rates based on the economy surrounding these states but the top slots belong to many southern states.

ggplot() + geom_polygon(data= map_loan_final, aes(x=long, y=lat, group = group, fill=map_loan_final$averageLA),colour="white") +
  scale_fill_continuous(low = "royalblue1", high = "royalblue4", guide="colorbar") +
  theme_bw()  +
  labs(title = "Average loan disbursement based on prosper data across the US",fill = "Average disbursed amount") +
  scale_y_continuous(breaks=c()) + 
  scale_x_continuous(breaks=c()) + 
  theme(panel.border =  element_blank())

Now when it comes to the highest disbursed amount through out the states, the largest average amounts were given to indiviudals from surprisingly the Distrivct of columbia. Alaska and New jersey the other states which recieved big amounts.

summary(map_loan$averageLA)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    4154    7979    8381    8290    8880   10251

Iowa, North Dokota and Maine were states with low disbursement of money.

Further Investigations

My hope is that this analyses can be be transfered into a linear model later on which would be my next task on Prosper Scores. The real data set has 81 variables so i without looking at the rest of them and getting them into the research as well it will be hard to make a good analytical model.